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International Conference on Machine Learning and big Data Analytics, ICMLbDA 2023

作者： vamsi Kumar Appala, satya Jagadeesh, s.v.v.D. Durga satish, M. sridevi, b. Department of Computer Science B V Raju College Bhimavaram AP Vishnupur India Department of Computer Science & Engineering Vishnu Institute of Technology Bhimavaram Vishnupur India

ISBN: (纸本)9783031513374

bones play a significant role in human body support, allowing us to use our muscles to walk, ride a bike, and hold a child. A bone fracture is a medical condition that occurs when a bone is cracked or broken due to an accident or trauma. Fractures can range in severity from a small crack in the bone to a complete break, where the bone is broken into two or more pieces. Accidents are one of the most common causes of bone fractures, and they can occur in a variety of ways, such as falls, sports injuries, car accidents, and workplace accidents. The severity of the fracture depends on the amount of force applied to the bone, the location of the fracture, and the age and health of the person. To deal with this problem, it requires an essential need of prediction system for the detection of bone fractures in the human body. The artificial intelligence subfield of machine learning offers distinguished assistance in event prediction and trains its models using data from real-world occurrences. In this chapter, ML and DL algorithms for predicting bone fractures using support vector machine, decision tree, random forest, and convolutional neural network (CNN) algorithms are discussed. Out of these, svM outperformed with 86% accuracy. © The Author(s), under exclusive license to springer Nature switzerland AG 2025.

关键词： Convolutional neural networks

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Enhanced hyperspectral image classification using multi-scale residual depthwise separable convolutional networks with advanced feature extraction and selection techniques

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International Journal of Information Technology (singapore) 2025年 1-20页

作者： Medikonda, venkata Reddy Reddy, Tatireddy subba Yallamandaiah, s. Karthik, sajja Anil, Chintalapudi Department of Computer Science and Engineering Marri Laxman Reddy Institute of Technology and Management Telangana Hyderabad 500043 India Department of Computer Science and Engineering B. V. Raju Institute of Technology Telangana Narsapur 502313 India Department of Electronics and Communication Engineering Vignan’s Nirula Institute of Technology & Science for Women Andhra Pradesh Guntur 522009 India Department of Computer Science and Engineering R.V.R & J.C College of Engineering Chowdavaram Andhra Pradesh Guntur 522019 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh 522302 India

Hyperspectral image classification faces significant challenges due to high-dimensional data and variability, often leading to inefficiencies in processing and reduced classification accuracy. Existing methods struggle with handling high-dimensional data, exhibit limited generalization capabilities, and lack efficiency in feature selection. To address these issues, this research proposes an advanced deep learning-based approach that integrates noise reduction, contrast enhancement, and min–max normalization for preprocessing. Feature extraction is performed using an Improved ResNet50 model, while optimal feature selection is achieved through the Improved Artificial Rabbits Optimization (IARO) algorithm, which effectively reduces dimensionality while preserving essential features. Classification is carried out using a Multi-scale Residual Depthwise separable Convolutional Network (MRDsCNN), leveraging residual learning and depthwise separable convolutions to enhance performance. The proposed approach significantly improves data quality and model accuracy, providing a robust and efficient solution for hyperspectral image classification. These innovations collectively address the limitations of traditional methods, offering a more reliable and effective classification system. © bharati vidyapeeth's Institute of computer Applications and Management 2025.

关键词： Contrast enhancement model Feature extraction Hyperspectral imaging classification Improved artificial rabbits optimization algorithm Multi-scale residual depthwise separable convolutional neural network

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Optimized Task scheduling in Diverse Distributed systems with a Hybrid Genetic Algorithm Approach 3rd

Optimized Task Scheduling in Diverse Distributed Systems wit...

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3rd International Conference on Intelligent systems and sustainable Computing, ICIssC 2023

作者： Muthusamy, Ramesh Kumar Kadu, swati Ayyasamy, Karthikeyan Pichuka veera venkata satya, srinivas Arumugam, Ramachandran Ammasai, viswanathan sengan, sudhakar Department of Information Technology V.S.B. College of Engineering Technical Campus Tamil Nadu Coimbatore642109 India Department of AI&DS AISSMS IOIT Pune411001 India Department of Computer Science and Engineering Panimalar Engineering College Tamil Nadu Chennai600123 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram522502 India Department of Computer Science and Engineering Saveetha Engineering College Tamil Nadu Chennai602105 India School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore632014 India Department of Computer Science and Engineering PSN College of Engineering and Technology Tamil Nadu Tirunelveli627012 India

ISBN: (纸本)9789819783540

In Heterogeneous Distributed systems (HDs), efficient task scheduling (Ts) across diverse resources remains a pressing challenge. This paper introduces a Hybrid Genetic Algorithm (HGA) as a solution, blending heuristic techniques with the evolutionary strengths of Genetic Algorithms (GAs) to optimize Task scheduling (Ts). Traditional algorithms often fall short in balancing computational speed with solution quality, especially in large-scale environments. This proposed HGA addresses this by leveraging domain-specific heuristics to provide a promising starting point for the GA's optimization process. The Ts dilemma has been successfully defined by an all-encompassing logical system, which ensures the accuracy of the design of algorithms. In comparison tests, the HGA frequently comes out on top, demonstrating less makespan and a more excellent distribution of tasks among the resources available. Also, resources with more efficient processing capabilities will be provided priority in this recommended heuristic method, which focuses on tasks with higher computational demands. The findings of the present investigation demonstrate that the HGA may transform Ts approaches by providing high-quality solutions while increasing performance. © The Author(s), under exclusive license to springer Nature singapore Pte Ltd. 2025.

关键词： scheduling algorithms

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Deep Learning in Analyzing Carbon Flux Patterns for Environmental Health: Remote sensing Insights for Climate Mitigation strategies

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Remote sensing in Earth systems sciences 2025年第2期8卷 337-351页

作者： subramani, Hariharan Harish, M. Neela s, selva kumar Radhakrishnan, P. Madupu, L. N. K. sai sandeep, Kasturi sai Nimma, Divya Department of CSE Panimalar Engineering College Chennai India Department of Biomedical Engineering Easwari Engineering College Chennai India Department of Computer Science and Engineering B.M.S College of Engineering Bull Temple Road Bengaluru India Department of ECE Tagore Engineering College Chennai India Department of Civil Engineering R.V.R. & J.C. College of Engineering Andhra Pradesh Chowdavaram Guntur India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Guntur Dist Andhra Pradesh India University of Southern Mississippi Data Analyst in UMMC Hattiesburg United States

The integration of deep learning with remote sensing techniques has emerged as a transformative approach in analyzing carbon flux patterns to mitigate climate change impacts on environmental health. This study introduces a spatiotemporal Attention Network (sATN) that leverages satellite-derived data and ground-based observations to predict and understand carbon flux variations. Key features such as vegetation indices, soil moisture, and land surface temperature are prioritized, contributing over 60% to the model’s predictive accuracy. The proposed sATN achieved a remarkable R<sup>2sup> score of 0.95, outperforming traditional models like ConvLsTM (0.87) and Transformers (0.90). With a low RMsE of 0.045 and MAE of 0.031, the sATN demonstrated robust accuracy and efficiency, achieving a balanced F1-score of 0.91. The results highlight spatial hotspots like the Amazon Rainforest and temporal trends indicating seasonal vegetation growth and human-induced emissions as critical contributors to carbon flux dynamics. by identifying regions and periods of high carbon activity, this study provides actionable insights for reforestation efforts, urban emissions control, and climate resilience planning. The sATN framework sets a new benchmark for integrating machine learning with environmental science, paving the way for innovative climate mitigation strategies. © The Author(s), under exclusive licence to springer Nature switzerland AG 2025.

关键词： Artificial intelligence Carbon flux Carbon sequestration Climate change Deep learning Environmental monitoring Remote sensing satellite data spatiotemporal Attention Network vegetation indices

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Detection of Driver Drowsiness Using Adaptive Eye Characteristic Ratio for Enhanced Road safety

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IEEE Access 2025年 13卷 84313-84328页

作者： Lamba, Puneet singh Jain, Rimjhim Khan, shakir Alanazi, sultan M. Jain, Achin Zamani, Abu Taha Panwar, Arvind Abdallah, Alsadig Mohammed Adam University of Delhi S.G.T.B Khalsa College Department of Computer Science Delhi110007 India University of North Carolina Charlotte United States Imam Mohammad Ibn Saud Islamic University College of Computer and Information Sciences Information Technology Department Riyadh Saudi Arabia Northern Border University Department of Department of Computer Science Faculty of Science Arar Saudi Arabia Bharati Vidyapeeth’s College of Engineering Department of Information Technology New Delhi India Galgotias University School of Computer Science and Engineering Uttar Pradesh Greater Noida India

Every year, countless people lose their lives in serious car accidents, and drowsy driving is a major cause. However, because the earliest indications of exhaustion can be identified before a dangerous scenario develops, the detection and signaling of driver drowsiness are ongoing research topics. This study presents a real-time detection system designed to detect driver’s sleep and deliver timely warnings. The mechanism employs an eye monitoring system that uses eye feature points to detect whether a driver’s eye is tired and delivers an alert if the driver is drowsy. The proposed work recognizes the driver’s face by collecting facial landmarks and gives warning to the driver to avoid crashes by performing adaptive thresholding and drowsiness diagnoses based on Eye Characteristic Ratio. The empirical results show that using random forest classifier, decision tree classifier, KNN classifier, and multinomial classifier, the proposed model achieved 90.48%, 89.44%, 88.63%, and 84.32% accuracy, respectively. © 2013 IEEE.

关键词： Highway accidents

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Permissioned blockchain security Pqfabric from Quantum and Classical Attacks 1

Permissioned Blockchain Security Pqfabric from Quantum and C...

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1st International Conference on Advances in computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Tamilarasi, C. bhavadrarani, K. Kittappa, Thiagarajan subashini, N. Anitha, v. vimalraj, E. B.S.Abdur Rahman Crescent Institute of Science & Technology Department of Computer Science and Engineering Chennai India V.S.B. Engineering College Department of Computer Science and Engineering Karur India Rajalakshmi Institute of Technology Department of Mathematics Chennai600124 India Sathyabama Institute of Science and Technology Department of Mathematics Chennai600119 India Panimalar Engineering College Department of CSE Poonamallee Chennai600123 India School of Computing Mohan Babu University Andhra Pradesh Tirupati India

ISBN: (纸本)9798331518578

Old style cryptographic techniques are genuinely compromised by the development of quantum computing, which requires the formation of safety ideal models that are impervious to quantum mistakes. A new permissioned blockchain security system called PqFabric is intended to oppose both traditional and quantum attacks. PqFabric ensures the validness, mystery, and respectability of exchanges in a quantum-empowered future by including post-quantum cryptographic techniques like hash-based and grid based marks. As opposed to ordinary blockchains, PqFabric utilizes a half breed agreement process that gets information trades and members characters by melding byzantine Fault Tolerance (bFT) with quantum-safe encryption. The system gives protection against calculation based attacks by shor and Grover, tending to huge imperfections in existing blockchain structures. besides, when quantum-safe guidelines advance, its secluded plan empowers smooth enhancements. With this procedure, PqFabric is situated as a ground breaking answer for organizations and areas wishing to safeguard their blockchain networks from new cryptographic risks. © 2025 IEEE.

关键词： Quantum computers

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Decentralized and Distributed Control strategies for Microgrids: A Review of Key Techniques and Applications

Decentralized and Distributed Control Strategies for Microgr...

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2025 IEEE International students' Conference on Electrical, Electronics and computer science, sCEECs 2025

作者： sarathkumar, D. Maheswari, P. Manivel, Murugesan Jayakumar, T. sivakumar, R. Parrthipan, b.K. Kongu Engineering College Department of Electrical and Electronics Engineering Tamil Nadu 638060 India Excel Engineering College Department of Electrical and Electronics Engineering Tamil Nadu 638052 India Karpagam Institute of Technology Department of Electrical and Electronics Engineering Tamilnadu Coimbatore641021 India Nandha Engineering College Department of Electrical and Electronics Engineering Tamil Nadu 638052 India V.S.B Engineering College Department of Electrical and Electronics Engineering Tamil Nadu 639111 India Kamaraj College of Engineering and Technology Department of Mechanical Engineering Tamil Nadu Vellakulam625701 India

ISBN: (纸本)9798331529833

Microgrids have emerged as a key solution for enhancing the flexibility, reliability, and sustainability of power systems. As the penetration of renewable energy sources and distributed generation increases, effective control strategies are essential for ensuring stable and efficient microgrid operation. This paper presents a comprehensive review of decentralized and distributed control strategies for microgrids, highlighting their significance in enabling scalability, robustness, and resilience. Key techniques, such as primary, secondary, and tertiary control layers, are discussed, with a focus on the transition from centralized to decentralized paradigms. The review explores the application of advanced algorithms like multi-agent systems (MAs), consensus-based control, and artificial intelligence (AI) for optimizing power sharing, voltage regulation, frequency stability, and energy management in islanded and grid-connected modes. Additionally, the paper analyzes the challenges and opportunities in implementing decentralized and distributed control, with real-world case studies and simulation-based insights to demonstrate their effectiveness in various microgrid configurations. Finally, potential future research directions are suggested to address emerging trends such as peer-to-peer energy trading, blockchain-based energy systems, and the integration of electric vehicles (Evs) into microgrid networks. © 2025 IEEE.

关键词： Decentralized control

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Wireless Charging Technologies for Electric vehicles: state of the Art and Future Outlook

Wireless Charging Technologies for Electric Vehicles: State ...

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2025 IEEE International students' Conference on Electrical, Electronics and computer science, sCEECs 2025

作者： sarathkumar, D. Parrthipan, b.K. Manivel, Murugesan Jayakumar, T. Rao, R subramani sambooranalaxmi, s. Department of Electrical and Electronics Engineering Tamil Nadu 638060 India Kamaraj College of Engineering and Technology Department of Mechanical Engineering Tamil Nadu Vellakulam625701 India Karpagam Institute of Technology Department of Electrical and Electronics Engineering Tamilnadu Coimbatore641021 India Nandha Engineering College Department of Electrical and Electronics Engineering Tamilnadu 638052 India V.S.B Engineering College Department of Electrical and Electronics Engineering Karur India Psr Engineering College Department of Electronics and Communication Engineering Tamil Nadu Sivakasi626140 India

ISBN: (纸本)9798331529833

Wireless charging technology for electric vehicles (Evs) represents a significant advancement in the transition to sustainable transportation, offering a convenient and efficient alternative to traditional plug-in charging systems. This paper provides a comprehensive review of the state-of-the-art wireless charging techniques, with a focus on inductive power transfer (IPT), resonant inductive coupling, and emerging capacitive and magnetic resonance methods. Key components of wireless charging systems, including power electronics, coil design, alignment techniques, and communication protocols, are discussed. We also address the challenges related to efficiency, safety, standardization, and scalability, particularly in dynamic charging systems designed for highway applications. The paper further explores the integration of wireless charging into smart grids, considering the potential for vehicle-to-grid (v2G) applications and renewable energy sources. A comparative analysis of existing technologies and their readiness for widespread adoption is provided, alongside an outlook on future trends, including high-power charging, autonomous charging infrastructure, and advances in materials and control algorithms. The paper concludes by identifying research gaps and potential directions for innovation in wireless charging for Evs. © 2025 IEEE.

关键词： Wireless charging

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A Resourceful Energy Management and Improved security in Wireless sensor Networks by Optimized Energy-based security Prototype

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sN computer science 2025年第3期6卷 1-13页

作者： Arya, Nancy Deore, shailesh shivaji sharma, Mahendra verma, Amit Patibandla, R. s. M. Lakshmi Krishna, K. Murali Pareek, Piyush Kumar Department of Computer Science and Engineering School of Engineering & Sciences GD Goenka University Sohna Gurugram India Department of Computer Engineering SSVPS B S DEORE College of Engineering Maharashtra Dhule India DoIT IMT College of Engineering UP Greater Noida India Department of Computer Science University Centre for Research and Development Chandigarh University Gharuan Punjab Mohali India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh Guntur India Department of Electronics Communication Engineering Rajalakshmi Engineering College TN Chennai India Department of AIML and IPR Cell Nitte Meenakshi Institute of Technology Bengaluru India

One must admit that while using a number of remote sensing points with limited nodes, it is crucial to optimize resource usage as intensely as possible and have a secure network with the least compromises, even though WsNs are applied in increasing numbers of industries. When deploying WsN, power utilization and ability to withstand a number of threats and attacks are important factors to be considered. This work presents a technique for a routing protocol that determines the shortest path between two nodes in a network while at the same time trying to conserve energy. Lifetime and energy consumption of the networks are two important factors when designing new protocols for WsN. An important problem of finding a load-sharing routing method with less energy consumption is difficult. TbEbR routing protocol optimizes the parameters that lead to minimal energy dissipation to enhance the lifetime of the network by identifying the path with the least energy consumption possible. Throughput, overhead, packet loss and end to end delay were used as performance parameters to assess the routing protocol. The analysis of the overall efficiency of the routing concept as well as the method of its application was described. As far as the performance of network and nodes is concerned, it is clear that TbEbR outperforms both DsR and AODv. For WsNs, the proposed routing protocol aims at minimizing the energy issue, which is a disadvantage of the technology. © The Author(s), under exclusive licence to springer Nature singapore Pte Ltd. 2025.

关键词： Dos attack EWMA Unit energy requirement WsN

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Analysis of brain Health Using Machine Learning and Artificial Intelligence Technology: Modern Drug Discovery Perspective 1

Analysis of Brain Health Using Machine Learning and Artifici...

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1st International Conference on Advances in computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Thirunagalingam, Arunkumar Usha, M.s. Geetha, s. Dhanamathi, A. Dhivya, s. Thendral, M.Tamil Business Intelligence and Reporting B. S. Abdur Rahman Crescent Institute of Science and Technology Department of CSE Chennai600048 India VS.B. ENGINEERING COLLEGE Department of Computer Science and Engineering Karur India Roever Engineering College Department of CSE Perambalur621220 India Sri Venkateswara College of Engineering Department of Computer Science and Engineering Sriperumbudur India Bannari Amman Institute of Technology Department of Information Technology Sathyamangalam India

ISBN: (纸本)9798331518578

The creation and discovery of pharmaceuticals may be considered the most important translational science activity that improves human invulnerability and happiness. In the pharmaceutical sector, strategies to reduce costs and speed up the development of new drugs have sparked a rigorous and fascinating brainstorming session. The use of classified big data in conjunction with remarkably improved computer power and cloud storage has enabled the application of artificial intelligence (AI), particularly the deep-learning (DL) component, in all domains. In healthy individuals, ML evaluation of neuroimaging data may accurately predict ordered age;illness and mental impairment have been linked to departures from normal brain maturation. Convolutional neural networks (CNN), a DL-based predictive display technique, were applied to both pre-handled and raw T1-weighted X-ray data in order to further evaluate the eligibility of 'brain-predicted age' as a biomarker of individual contrasts in the brain ageing process. brain-predicted age is a very accurate, remarkably stable, and genetically valid trait that exhibits assurance as a biomarker of brain maturation. Additionally, age may be accurately predicted from raw T1-MRI data, which greatly reduces the amount of time needed to compute fresh data and accelerates the process of delivering immediate data on brain health in clinical settings. © 2025 IEEE.

关键词： Electronic health record

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